org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 10.0 failed 4 times, most recent failure: Lost task 0.3 in stage 10.0 (TID 34, ip-172-31-0-28.ec2.internal): org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.

Stack Overflow | Newbiee | 3 months ago
  1. 0

    SPARK-5063 RDD transformations and actions can only be invoked by the driver

    Stack Overflow | 3 months ago | Newbiee
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 10.0 failed 4 times, most recent failure: Lost task 0.3 in stage 10.0 (TID 34, ip-172-31-0-28.ec2.internal): org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
  2. 0

    Spark RDD transformations exception when accessing MapPartitionsRDD

    Stack Overflow | 10 months ago | Robert Egginton
    org.apache.spark.SparkException: Job aborted due to stage failure: Task 2 in stage 11500.0 failed 1 times, most recent failure: Lost task 2.0 in stage 11500.0 (TID 6401, localhost): org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
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    Apache Spark User List - Can it works in load the MatrixFactorizationModel and predict product with Spark Streaming?

    nabble.com | 6 months ago
    org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.
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  5. 0

    spark programming with scala, how to do a lookup

    Stack Overflow | 1 month ago | eigerprog
    org.apache.spark.SparkException: This RDD lacks a SparkContext. It could happen in the following cases: (1) RDD transformations and actions are NOT invoked by the driver, but inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063. (2) When a Spark Streaming job recovers from checkpoint, this exception will be hit if a reference to an RDD not defined by the streaming job is used in DStream operations. For more information, See SPARK-13758.
  6. 0

    GitHub comment 4#165082061

    GitHub | 12 months ago | rql123
    org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.

  1. tyson925 17 times, last 5 months ago
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Root Cause Analysis

  1. org.apache.spark.SparkException

    Job aborted due to stage failure: Task 0 in stage 10.0 failed 4 times, most recent failure: Lost task 0.3 in stage 10.0 (TID 34, ip-172-31-0-28.ec2.internal): org.apache.spark.SparkException: RDD transformations and actions can only be invoked by the driver, not inside of other transformations; for example, rdd1.map(x => rdd2.values.count() * x) is invalid because the values transformation and count action cannot be performed inside of the rdd1.map transformation. For more information, see SPARK-5063.

    at org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc()
  2. Spark
    PairRDDFunctions.lookup
    1. org.apache.spark.rdd.RDD.org$apache$spark$rdd$RDD$$sc(RDD.scala:87)
    2. org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
    3. org.apache.spark.rdd.PairRDDFunctions.lookup(PairRDDFunctions.scala:928)
    3 frames
  3. Unknown
    $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply
    1. $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:89)
    2. $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:83)
    2 frames
  4. Scala
    AbstractIterator.toArray
    1. scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
    2. scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
    3. scala.collection.Iterator$class.foreach(Iterator.scala:727)
    4. scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    5. scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    6. scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    7. scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    8. scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    9. scala.collection.AbstractIterator.to(Iterator.scala:1157)
    10. scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    11. scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    12. scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    13. scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    13 frames
  5. Spark
    Executor$TaskRunner.run
    1. org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
    2. org.apache.spark.rdd.RDD$$anonfun$take$1$$anonfun$28.apply(RDD.scala:1314)
    3. org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    4. org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
    5. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    6. org.apache.spark.scheduler.Task.run(Task.scala:89)
    7. org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
    7 frames
  6. Java RT
    Thread.run
    1. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    2. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    3. java.lang.Thread.run(Thread.java:745)
    3 frames